The rapid expansion of cloud computing has led to an increasing demand for efficient resource management techniques, especially as cloud environments grow in complexity and scale. Among the various approaches for optimizing resource allocation, artificial intelligence (AI)-based algorithms have shown significant promise due to their ability to learn from data and adapt to dynamic workloads. This paper presents a comprehensive review of the Gated Graph Convolutional Network (GGCNA) and other AI-based algorithms applied to resource management in cloud computing. We explore the key challenges in cloud resource management, such as load balancing, energy efficiency, and Quality of Service (QoS) optimization. The paper also highlights the advantages and limitations of AI-based models, focusing on their ability to address issues like scalability, adaptability, and real-time decision-making. Through a detailed comparison of various AI techniques, including reinforcement learning, deep learning, and GGCNA, we aim to provide insights into their effec-tiveness in optimizing resource allocation and improving overall cloud computing performance. Finally, we discuss the future directions of research in this field, emphasizing the potential of AI-driven solutions for sustainable and efficient cloud resource management.

Gated Graph Convolutional Networks (GGCNA), AI-Based Algorithms, Resource Management, Cloud Computing, Op-timization Techniques